Search Results (1,541)

Cushing’s disease (CD) is a rare neuroendocrine disorder caused by ACTH-secreting pituitary adenomas, presenting significant diagnostic and therapeutic challenges. Given the evolutionary conservation of the hypothalamic–pituitary–adrenal axis, this review explores the translational value of spontaneous CD forms in dogs, horses, cats, small mammals, and rats, as well as of experimental models in mice, rats, and zebrafish. Dogs are the most studied, showing strong molecular and clinical similarities with human CD, making them valuable for preclinical drug and diagnostic research. While equine and feline CD are less characterized, they may provide insights into dopaminergic therapies and glucocorticoid resistance. Nevertheless, practical and ethical challenges limit the experimental use of companion animals. In preclinical research, mouse models are widely used to study hypercortisolism and test therapeutic agents via transgenic and xenograft strategies. Conversely, few studies are available on a zebrafish transgenic model for CD, displaying pituitary corticotroph expansion and partial resistance to glucocorticoid-negative feedback at the larval stage, while adults exhibit hypercortisolism resembling the human phenotype. Future transplantable systems in zebrafish may overcome several limitations observed in mice, supporting CD research. Collectively, these animal models, each offering unique advantages and limitations, provide a diverse toolkit for advancing CD research and improving human clinical outcomes. Full article

Physical rehabilitation is crucial for injury recovery, offering pain relief and faster healing. However, traditional methods rely heavily on in-person professional feedback, which can be time-consuming, expensive, and prone to human error, limiting accessibility and effectiveness. As a result, patients are often encouraged to perform exercises at home; however, due to the lack of professional guidance, motivation dwindles and adherence becomes a challenge. To address this, this paper proposes a smartphone-based solution that enables patients to receive exercise feedback independently. This paper reviews current Computer Vision systems for assessing rehabilitation exercises and introduces an intelligent system designed to assist patients in their recovery. Our proposed system uses motion tracking based on Computer Vision, analyzing videos recorded with a smartphone. With accessibility as a priority, the system is evaluated against the advanced Qualysis Motion Capture System using a dataset labeled by expert physicians. The framework focuses on human pose detection and movement quality assessment, aiming to reduce recovery times, minimize human error, and make rehabilitation more accessible. This proof-of-concept study was conducted as a pilot evaluation involving 15 participants, consistent with earlier work in the field, and serves to assess feasibility before scaling to larger datasets. This innovative approach has the potential to transform rehabilitation, providing accurate feedback and support to patients without the need for in-person supervision or specialized equipment. Full article

Educational games often fail to effectively merge game mechanics with educational goals, lacking adaptive feedback and real-time performance monitoring. This study explores how Human–Computer Interaction principles and adaptive feedback can enhance educational game design to improve learning outcomes and user experience. Four educational games were analyzed using a mixed-methods approach and evaluated through established frameworks, such as the Serious Educational Games Evaluation Framework, the Assessment of Learning and Motivation Software, the Learning Object Evaluation Scale for Students, and Universal Design for Learning guidelines. In addition, a novel Human–Machine Pair Inspection protocol was employed to gather real-time data on adaptive feedback, cognitive load, and interactive behavior. Findings suggest that Human–Machine Pair Inspection-based adaptive mechanisms significantly boost personalized learning, knowledge retention, and student motivation by better aligning games with learning objectives. Although the sample size is small, this research provides practical insights for educators and designers, highlighting the effectiveness of adaptive Game-Based Learning. The study proposes the Human–Machine Pair Inspection methodology as a valuable tool for creating educational games that successfully balance user experience with learning goals, warranting further empirical validation with larger groups. Full article

This paper addresses the urgent need to redefine education strategies for the Net Generation in the context of rapid technological and societal changes. First, the educational challenge is placed within a broader philosophical and cultural framework, focusing on the fluid and evolving nature of knowledge and human experience. Building on the paradigm shift from Web 2.0 to Web 4.0 and the emergence of Education 5.0, this paper investigates the pedagogical implications of these developments. Through conceptual analysis supported by contemporary educational theory, this paper proposes a model of education that integrates personalized learning, real-time feedback, and collaborative, interdisciplinary environments. A special focus is placed on the role of educators as mentors, rather than mere transmitters of information, and on the ethical, social, and emotional dimensions of digital learning. This article highlights the importance of adjusting educational practices to real-life contexts and future challenges of young learners while ensuring that the humanistic essence of education is not lost. Full article

This paper presents the design and evaluation of a sound-driven robot quiz system that enhances fairness and engagement in educational human–robot interaction (HRI). The system integrates a real-time sound-based first-responder detection mechanism with gamified multimodal feedback, including verbal cues, music, gestures, points, and badges. Motivational design followed the Octalysis framework, and the system was evaluated using validated scales from the Technology Acceptance Model (TAM), the Intrinsic Motivation Inventory (IMI), and the Godspeed Questionnaire. An experimental study was conducted with 32 university students comparing the proposed multimodal system combined with sound-driven first quiz responder detection to a sequential turn-taking quiz response with a verbal-only feedback system as a baseline. Results revealed significantly higher scores for the experimental group across perceived usefulness (M = 4.32 vs. 3.05, d = 2.14), perceived ease of use (M = 4.03 vs. 3.17, d = 1.43), behavioral intention (M = 4.24 vs. 3.28, d = 1.62), and motivation (M = 4.48 vs. 3.39, d = 3.11). The sound-based first-responder detection system achieved 97.5% accuracy and was perceived as fair and intuitive. These findings highlight the impact of fairness, motivational feedback, and multimodal interaction on learner engagement. The proposed system offers a scalable model for designing inclusive and engaging educational robots that promote active participation through meaningful and enjoyable interactions. Full article

Despite rapid progress in natural language processing, current interactive AI systems continue to struggle with interpreting ambiguous, idiomatic, and contextually rich human language, a barrier to natural human–computer interaction. Many deployed applications, such as language games or educational tools, showcase surface-level adaptation but do not systematically probe or advance the deeper semantic understanding of user intent in open-ended, creative settings. In this paper, we present Semantrix, a web-based semantic word-guessing platform, not merely as a game but as a living testbed for evaluating and extending the semantic capabilities of state-of-the-art Transformer models in human-facing contexts. Semantrix challenges models to both assess the nuanced meaning of user guesses and generate dynamic, context-sensitive hints in real time, exposing the system to the diversity, ambiguity, and unpredictability of genuine human interaction. To empirically investigate how advanced semantic representations and adaptive language feedback affect user experience, we conducted a preregistered 2 × 2 factorial study (N = 42), independently manipulating embedding depth (Transformers vs. Word2Vec) and feedback adaptivity (dynamic hints vs. minimal feedback). Our findings revealed that only the combination of Transformer-based semantic modelling and adaptive hint generation sustained user engagement, motivation, and enjoyment; conditions lacking either component led to pronounced attrition, highlighting the limitations of shallow or static approaches. Beyond benchmarking game performance, we argue that the methodologies applied in platforms like Semantrix are helpful for improving machine understanding of natural language, paving the way for more robust, intuitive, and human-aligned AI approaches. Full article

The implementation of vegetation restoration projects in the Three-North Sandstorm Region has provided a critical opportunity for ecological rehabilitation. A thorough exploration of the mutual feedback mechanisms between land use and vegetation dynamics holds significant guiding value for ecological restoration practices. This study innovatively integrates the Hurst index, trend analysis, and significance testing to systematically analyze the characteristics of vegetation dynamics and their responses to land-use changes from 1990 to 2020. By constructing a multidimensional evaluation system, including the comprehensive land-use index (L) and land-use change rate (R), this research is the first to quantitatively reveal the relationship between land-use characteristics and the Normalized Difference Vegetation Index (NDVI) across different desertification regions. Key findings include: (1) Vegetation in the study area exhibited a significant improvement trend, with 92.12% of regions showing a Hurst index > 0.5, indicating strong persistence in vegetation recovery. (2) Land-use transitions revealed a consistent increase in construction land across all sandy regions, accompanied by the conversion of unused land to grassland. (3) The fluctuation of R gradually narrowed from 1990 to 2020, while the L demonstrated a significant upward trend. (4) The R generally exerted a negative influence on NDVI, whereas the L exhibited a threshold effect. Specifically, NDVI increased with rising L when L < 215 (e.g., MWSD, KBQD regions), but declined when L exceeded 215 (e.g., SNS region). This suggests that frequent land-use changes hinder vegetation growth, while moderate increases in land-use intensity initially promote biomass accumulation—until a critical threshold is surpassed, beyond which further intensification exerts adverse effects. One-way ANOVA and significance testing further demonstrated that climate predominantly drove NDVI increases in cropland, forest, shrubland, and grassland, whereas human activities played a decisive role in vegetation growth within unused or sparsely vegetated areas. This study not only quantitatively identifies critical thresholds for land-use impacts on vegetation but also provides a scientific foundation for targeted ecological restoration strategies, offering practical insights for reconciling ecological conservation with land-use demands. Full article

Marsh wetland degradation and shrub expansion, driven by human activities and climate change, can impact carbon, nitrogen, and sulfur cycles by soil microorganisms. There is a paucity of systematic and in-depth research on the impact of shrub expansion in temperate wetlands on soil element cycles, which is a pressing scientific issue that demands resolution. This study used metagenomic sequencing and soil analysis methods to investigate the impact of shrub expansion in the Sanjiang Plain wetlands on carbon, nitrogen, and sulfur cycles in temperate wetland soils, as well as on functional microbial communities. Shrub expansion significantly altered soil carbon, nitrogen, and sulfur cycle processes and the composition (β diversity) of associated functional microbial communities, despite minimal changes in overall α diversity. Significant shifts occurred in the abundance of cycle pathways and related functional genes. Ammonia nitrogen, moisture, and total phosphorus were identified as the primary factors influencing these cycles and the functional microbial communities. Changes in the abundance of specific cycling pathways following shrub expansion are key drivers of functional community structure transformation. These changes may significantly reduce the long-term carbon sequestration potential of wetlands and affect regional climate feedback by altering greenhouse gas fluxes. The findings provide a theoretical basis for managing shrub expansion and assessing wetland function. Full article

Background: While widely investigated in implantology and nonsurgical endodontics, evidence on the application of dynamic navigation systems (DNSs) in endodontic surgery remains limited. This systematic review aimed to assess their accuracy and reliability based on two-dimensional and three-dimensional virtual deviations, osteotomy parameters, as well as procedural duration, the impact of the dentist’s level of expertise, endodontic surgery healing outcomes, complications, and dentist- and patient-reported feedback. Methods: Following the PRISMA guidelines, an electronic search was conducted across the PubMed/MEDLINE, Scopus, Web of Science, and PROSPERO (CRD420251056347) databases up to 23 April 2025. Eligible studies involved human subjects (cadaveric or live) undergoing endodontic surgery with dynamic navigation. Extracted data focused on accuracy metrics such as platform/apical depth deviation and angular deflection. Results: Fourteen studies involving 240 roots were included. DNSs showed high accuracy, with mean platform and apical deviations of 1.17 ± 0.84 mm and 1.21 ± 0.99 mm, respectively, and angular deflection of 2.29° ± 1.69°, as well as low global deviations, averaging 0.83 ± 0.34 mm at the platform and 0.98 ± 0.79 mm at the apex. Root-end resections averaged 3.02 mm in length and 7.49° in angle deviation. DNS-assisted steps averaged 5.6 ± 2.56 min. Healing outcomes were favorable and complications were infrequent. Conclusions: DNSs demonstrated satisfactory accuracy and efficiency and, in the included studies, were linked to favorable healing outcomes and a low occurrence of intra- and postoperative complications. Nevertheless, the current evidence is still limited by the small number of available studies, and the heterogeneity in study designs and outcome measures, highlighting the need for further studies to define the clinical implications of DNSs in endodontic surgery. Full article

Generating realistic and contextually appropriate gestures is crucial for creating engaging embodied conversational agents. Although speech is the primary input for gesture generation, adding controls like gesture velocity, hand height, and emotion is essential for generating more natural, human-like gestures. However, current approaches to controllable gesture generation often utilize a limited number of control parameters and lack the ability to activate/deactivate them selectively. Therefore, in this work, we propose the Cont-Gest model, a Transformer-based gesture generation model that enables selective control activation through masked training and a control fusion strategy. Furthermore, to better support the development of such models, we propose a novel evaluation-driven development (EDD) workflow, which combines several iterative tasks: automatic control signal extraction, control specification, visual (subjective) feedback, and objective evaluation. This workflow enables continuous monitoring of model performance and facilitates iterative refinement through feedback-driven development cycles. For objective evaluation, we are using the validated Kinetic–Hellinger distance, an objective metric that correlates strongly with the human perception of gesture quality. We evaluated multiple model configurations and control dynamics strategies within the proposed workflow. Experimental results show that Feature-wise Linear Modulation (FiLM) conditioning, combined with single-mask training and voice activity scaling, achieves the best balance between gesture quality and adherence to control inputs. Full article

Reliable and fast communication between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) is essential for effective coordination in agricultural settings, particularly when human involvement is part of the system. This study systematically compares two communication architectures representing centralized and decentralized communication frameworks: (a) MAVLink (decentralized) and (b) Farm Management Information System (FMIS) (centralized). Field experiments were conducted in both empty field and orchard environments, using a rotary UAV for worker detection and a UGV responding to intent signaled through color-coded hats. Across 120 trials, the system performance was assessed in terms of communication reliability, latency, energy consumption, and responsiveness. FMIS consistently demonstrated higher message delivery success rates (97% in both environments) than MAVLink (83% in the empty field and 70% in the orchard). However, it resulted in higher UGV resource usage. Conversely, MAVLink achieved reduced UGV power draw and lower latency, but it was more affected by obstructed settings and also resulted in increased UAV battery consumption. In conclusion, MAVLink is suitable for time-sensitive operations that require rapid feedback, while FMIS is better suited for tasks that demand reliable communication in complex agricultural environments. Consequently, the selection between MAVLink and FMIS should be guided by the specific mission goals and environmental conditions. Full article

Human-centered qualities (e.g., privacy, sense of orientation, etc.) significantly impact the social sustainability of buildings and the well-being of their occupants. However, due to their subjective nature, such qualities are often implicit and are not documented properly during the planning phase of construction projects. While several types of design intentions are documented throughout the lifecycle of building projects, intentions that are socially oriented and target soft aspects that reflect occupants’ experience (e.g., comfort, well-being, etc.), are evidently missing from current digital building models, hence risking constructing uninhabitable or socially unsustainable buildings. Through an extensive interdisciplinary collaboration between building scientists, practicing architects, and computer scientists, this paper addresses this gap by introducing a formalization framework, “ProFormalize”, to capture social design intentions (SDIs) in digital building models. This work presents a novel approach to digitalize SDIs in buildings, bridging a critical gap between architectural design intentions and explicit digital representations. Following a case-study-driven approach and a co-creation-based methodology, we developed the framework aiming to establish the foundations for developing a decision-support software tool (plugin) that enables architects, who are directly involved in the research process, to integrate SDIs into digital building models. The expert feedback demonstrates that the framework can make implicit SDIs explicit, which enables architects to integrate them into digital building models. Expert feedback suggested that a software tool developed based on this framework can enhance decision-making due to the traceability and analyzability of digital models. Full article

This case study examines the integration of artificial intelligence (AI) into undergraduate architectural education through a 2024–25 core studio teaching experiment at Zhejiang University. A dual-module framework was implemented, comprising a 20 h AI skills training module and in-class ethics discussions, without altering the existing studio structure. The AI skills module introduced deep learning models, LLMs, AIGC image models, LoRA fine-tuning, and ComfyUI, supported by a dedicated technical instructor. Student feedback indicated phase-dependent and tool-sensitive engagement, and students expressed a preference for embedded ethical discussion within the design studio rather than separate formal instruction. The experiment demonstrated that modular AI education is both scalable and practical, highlighting the importance of phase-sensitive guidance, balanced technical and ethical framing, and institutional support such as cloud platforms and research-based AI tools. The integration enhanced students’ digital adaptability and strategic thinking while prompting reflection on issues such as authorship, algorithmic bias, and accountability in human–AI collaboration. These findings offer a replicable model for AI-integrated design pedagogy that balances technical training with critical awareness. Full article

Rapid developments in intelligent interfaces across service, healthcare, and industry have led to unprecedented demands for advanced tactile perception systems. Traditional tactile sensors often struggle with adaptability on curved surfaces and lack sufficient feedback for delicate interactions. Flexible and wearable tactile sensors are emerging as a revolutionary solution, driven by innovations in flexible electronics and micro-engineered materials. This paper reviews recent advancements in flexible tactile sensors, focusing on their mechanisms, multifunctional performance and applications in health monitoring, human–machine interactions, and robotics. The first section outlines the primary transduction mechanisms of piezoresistive (resistance changes), capacitive (capacitance changes), piezoelectric (piezoelectric effect), and triboelectric (contact electrification) sensors while examining material selection strategies for performance optimization. Next, we explore the structural design of multifunctional flexible tactile sensors and highlight potential applications in motion detection and wearable systems. Finally, a detailed discussion covers specific applications of these sensors in health monitoring, human–machine interactions, and robotics. This review examines their promising prospects across various fields, including medical care, virtual reality, precision agriculture, and ocean monitoring. Full article

With the growing use of digital technologies and interactive games, there is rising interest in how people respond to challenges, stress, and decision-making in virtual environments. Studying human behavior in such settings helps to improve design, training, and user experience. Instead of relying on complex devices, Virtual Reality (VR) creates new ways to observe and understand these responses in a simple and engaging format. This study introduces a lightweight method for monitoring stress levels that uses VR as the primary sensing platform. Detection relies on behavioral signals from VR. A minimal sensor such as Galvanic Skin Response (GSR), which measures skin conductance as a sign of physiological body response, supports the Sensor-Assisted Unity Architecture. The proposed Sensor-Assisted Unity Architecture focuses on analyzing the user’s behavior inside the virtual environment along with physical sensory measurements. Most existing systems rely on physiological wearables, which add both cost and complexity. The Sensor-Assisted Unity Architecture shifts the focus to behavioral analysis in VR supplemented by minimal physiological input. Behavioral cues captured within the VR environment are analyzed in real time by an embedded processor, which then triggers simple physical feedback. Results show that combining VR behavioral data with a minimal sensor can improve detection in cases where behavioral or physiological signals alone may be insufficient. While this study does not quantitatively compare the Sensor-Assisted Unity Architecture to multi-sensor setups, it highlights VR as the main platform, with sensor input offering targeted enhancements without significantly increasing system complexity. Full article